Unlocking Value Through AI-Powered Revenue Growth Management in CPG

In today's volatile market, CPG companies face immense pricing challenges due to persistent inflation and rapidly shifting consumer behavior. Even seasoned analysts risk costly missteps in timing or adjusting prices, with each error potentially leading to significant revenue losses or market share declines.

Revenue growth management (RGM) is a strategic business framework that helps companies optimize revenue and profit through a comprehensive approach encompassing five key pillars: brand portfolio pricing, price pack architecture, mix management, promotion management, and trade term management. This integrated methodology goes beyond traditional pricing strategies to create a holistic view of how products should be priced, packaged, promoted, and distributed across different channels and markets.

Traditional RGM approaches have limitations in today's dynamic marketplace. Some of the challenges most enterprises face include the inability to capture information in real-time; operational silos due to business units using their own tools and processes without getting insights into the big picture; the inability to tie together insights from different events; error-prone manual processes for reconciliation and reporting; and a lack of granular level insights. 

RGM Maturity Evolution Through the Lens of Data & AI

RGM maturity evolves from basic pricing mechanics to AI-driven strategic decision-making. Initially, organizations use machine learning and predictive analytics for pricing elasticity and trade promotion management. In the intermediate stages, companies break down data silos, integrating simulations that account for variables such as consumer behavior, market conditions, and competition, leading to insights at the SKU level. Generative AI accelerates this process, enabling firms to shift from reactive pricing to proactive strategies by extracting insights from unstructured data. At the highest level of RGM maturity, decisions are dynamic, automated, and tailored to granular-level market segments, channels, and geographies.

Major differentiators of AI-powered RGM include:

Connecting disparate signals: Enterprises face the challenge of scattered data across formats, systems, and source,s such as retailers, partners, social media, and third-party platforms. To address this, AI can unify and organize this vast information, forming a comprehensive consumer profile or "consumer graph." By triangulating data like POS scans, and analyzing sentiments and preferences from reviews and media consumption, businesses can establish a foundational view for RGM. This unified consumer graph enables deeper insights into customer behavior and forms the basis for decision-making.

Advanced scenario planning: Generative AI models coupled with advanced econometric models can simulate multiple pricing scenarios based on historical data, market trends, and consumer sentiment. The efficacy is further boosted when using RGM domain-specific AI models that can understand the context of different queries.

A multinational corporation used an AI-led RGM tool to address pricing strategies for its CPG brands. For a brand with strong equity, the tool recommended a price increase to counter squeezed margins, leveraging its market position. For another brand, the AI identified over-indexed pricing relative to its equity and advised holding back on price increases, focusing instead on enhancing brand equity before adjusting prices.

Gen AI assistants for assisting RGM teams: Generative AI technologies simplify access to advanced analytics by transforming complex data into easy-to-understand narratives. This empowers RGM teams to make swift, informed decisions without relying on specialized data science expertise.

Agentic AI for dynamic pricing: Agentic AI — AI agents that act autonomously within predefined parameters — can monitor competitive pricing and market conditions in real-time and automate manual processes for data capture and reporting.

Infosys Equinox Strategic Pricing Leverages the Power of AI-Driven RGM

Among the five pillars, strategic pricing holds the greatest potential to drive measurable impact. Infosys offers next-generation revenue growth management for consumer brands through its Infosys Equinox Strategic Pricing solution, powered by the Consumer Surplus Factor (CSF).

This cloud-native solution utilizes advanced AI to simulate, implement, and monitor pricing and promotion strategies across brands, packs, and channels. It enables brands to achieve profitable growth, enhance customer engagement, and scale decision-making with data-driven insights. CSF tracks customer behavior, competitor pricing, and sales velocity in near real-time, leveraging a proprietary econometric AI model to measure pricing power and brand equity in competitive markets.

As we move into 2025, the message for CPG leaders is simple but urgent: the time to act is now. By embracing advanced RGM strategies and integrating cutting-edge AI like agentic AI and generative AI, businesses can turn pricing challenges into a catalyst for sustained success. After all, in a world of constant change, those who adapt intelligently will lead the way.